Development, nutritional evaluation and optimization of instant weaning porridge from broken rice fractions and bambara groundnut (Vigna subterranea (L.) Verdc) blends

  • N Danbaba
  • I Nkama
  • M.H. Badau
  • A.S. Ndindeng
Keywords: Rice, bambara groundnut, extrusion, process optimization, response surface methodology


In this study, different instant porridges were formulated from broken fractions of rice blended with bambara groundnut flour through extrusion cooking. Response Surface Methodology (RSM) and Central Composite Rotatable Design (CCRD) were used to optimize the production variables. The objective was to locate the best combination of barrel temperature (X1), feed moisture composition (X2) and feed bambara groundnut composition (X3) that will give optimum porridge having high nutrient density. Regression models and response surface plots were developed and their adequacy tested by examining coefficient of determination (R2, R2
adjusted), analysis of variance (ANOVA), lack-of-fit test and analysis of residual. ANOVA analysis indicated significant (p ≤ 0.05) effects of process variables on the nutritional quality of the porridge. Models indicated a non-significant lack-of-fit. Applying the desirability function, the best optimum levels for independent variables (X1, X2 and X3) for the production of instant porridge from rice-bambara groundnut formulations were found to be 120oC barrel temperature, 20 g/100 g feed moisture content and 22.40 g/100 g feed bamabara groundnut composition. At this combinations, optimum moisture, fat, protein, fibre, carbohydrate, and calorie value were 0.89%, 0.73%, 21.66%, 0.89%, 1.34%, 77.81 g/100 g and 394.33 kcal/ 100 g respectively, while optimum Mg, Mn, Fe, Cu, Zn and Ca were 13.82, 5.14, 12.12, 3.67, 5.13 and 25.81 mg/100 g respectively. The optimum product contained high protein and mineral and could be said to appropriate for weaning to reduce protein-energy malnutrition in Africa.

Keywords: Rice, bambara groundnut, extrusion, process optimization, response surface methodology


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eISSN: 0189-7241